Microarray data play a huge role in recognizing a proper cancer diagnosis and classification. In most microarray data set consist of thousands of genes, but the majority number of genes are irrelevant to the diseases. An efficient algorithm for gene selection becomes important to deal with large microarray data. The main challenge is to analyze and select the relevant genes with maximum classification accuracy. Various algorithms were proposed for gene classification in previous studies, however, limited success was succeeded due to the selection of many genes in the high-dimensional microarray data. This study proposed and developed a hybrid multi-objective cuckoo search with evolutionary operators for gene selection. Evolutionary operators that are used in this article were double mutation and single crossover operators. The motivation behind this research is to improve the dimensions' values and explorative search abilities. Multi-objective cuckoo search with evolutionary operators employed the selection of informative genes among the high-dimensional cancer microarray data. Experiments were conducted on seven publicly available and high-dimensional cancer microarray data sets. These microarray data sets consist of approximately 2000 to 15000 genes. The results from the experiments concluded that the developed algorithm, multi-objective cuckoo search with evolutionary operators outperforms cuckoo search and multi-objective cuckoo search algorithms with a smaller number of selected significant genes.

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<span>Manufacturing organizations implemented Business Intelligence (BI) due to many advantages offered by it. The lack of research on the acceptance of BI in manufacturing motivates the initiative in this study to have an understanding of the factors that influence the acceptance of BI in manufacturing sector. Therefore, the research proposes a model which indicates the acceptance factors of BI in manufacturing. An integrated model consisting of underlying models of Technology Acceptance Model (TAM), Expectation Confirmation Theory (ECT) and Task-Technology Fit (TTF) will be developed. The new model will formulate 19 hypotheses and 11 factors contributing to the continuance and acceptance of BI. The model will be tested using quantitative and qualitative survey conducted to Malaysian manufacturing companies and validated using Structural Equation Modelling (SEM) to investigate the causal and mediating relationships between the factors. The expected result is hoping to suggest that selected factors in the model are positively related towards the acceptance of BI in manufacturing. The results are also hoping to guide future initiatives by industrial practitioners to develop and distribute BI to the manufacturing market.</span>

Microarray analysis able to monitor thousands of gene expression data, however, to elucidate the hidden patterns in the data is a complex process. These gene expression data show its imprecision, noise and vagueness due to its high dimensional properties. There are a handful of clustering algorithms have been proposed to extract the important information from the gene expression data. However, identifying the underlying biological knowledge of the data is still hard. To acknowledge these issues, clustering algorithms are used to reduce the data complexity. In this article, hybrid of agglomerative hierarchical clustering and modified k-medoids (partitional clustering) are proposed. Application of the proposed of clustering algorithms to group the genes that have similar functionality which might assist pre-processing procedures. In order to emphasize the quality of the clustering results, cluster quality index (CQI) is determined. Lung and ovary data sets used and the method retrieved a fair clustering with CQI, 0.37 and 0.48 respectively. This research contributes by avoiding biasness toward genes and provide true sense of clustering output using the advantage of hierarchical and partitional clustering methods.